Determining the right number of subjects in neuroimaging studies is an important process as in any other biomedical studies. However, because of the 3D nature of neuroimaging data, calculating power and sample sizes remains to be a major challenge. Despite some pioneering attempts, to this date, there is no effective method for investigators to calculate power for neuroimaging studies. To address this issue, the PI of this project has developed a power calculation method specifically designed for neuroimaging data analyses. This method, based on random field theory (RFT), can model signals in neuroimaging data theoretically without time consuming simulations and resampling procedures. Furthermore, this method can visualize local variability of power in different areas of the brain in the form of power and sample size maps. Such maps can be a useful study planning tool for investigators to determine where signals are likely to be detected with how many subjects. Based on this RFT-based method, we will develop a user-friendly tool for power calculation for neuroimaging studies, and fully characterize its performance. In Specific Aim 1, we will focus on the actual development of a power calculation software tool with a graphical user interface (GUI). In Specific Aim 2, we will characterize the performance of our power calculation framework so that users will be able to appropriately interpret the resulting power maps. In particular, gold standard power maps will be generated by a resampling process from multiple functional and structural MRI data sets. These gold standard power maps will be compared to power maps generated based on mock pilot data (randomly-selected subset of the full data) with different sample sizes to determine the relationship between the accuracy and the size of pilot data. In Specific Aim 3, we will develop and implement some methodological frameworks in addition to the existing RFT-based method in order to increase the utility of our tool. Specifically, we will implement power calculation based on FDR (false discovery rate) method, which is a popular alternative to RFT-based methods in neuroimaging data analyses. In summary, the proposed tool is the first power analysis tool specialized for neuroimaging studies accounting for massive multiple comparisons, and can greatly simplify the study planning process. The potential use of this tool is not limited to functional neuroimaging, but also a variety of other neuroimaging modalities. Moreover, accurate sample size estimates can help reduce unnecessary scans and under-powered studies, saving time and costs associated with neuroimaging studies. PUBLIC HEALTH RELEVANCE: In recent years, neuroimaging has become an essential part of investigations of the brain, and increased our understanding of various neurological and psychiatric disorders, such as Alzheimer's disease and schizophrenia. The proposed project will help investigators of such studies to allocate time and resources more efficiently, by accurately determining the number of subjects required for their studies.